Homeostatic image perception: An artificial
system, T. Feldman and L.
Younes, Computer Vision and Image Understanding 102
In this paper, we build a low-level "vision system" in successive tiers. The image is transformed into a series or ternary fields that collect coarse local information, only storing whether the image response to a local filter is lower than a high threshold, or smaller than a low threshold, or neither (first image). A Markov random field is then trained to learn a join distribution for these layers, within a finite field of view(second image). This model is then used as a saliency detector, selecting image patches that are atypical with respect to the learned model.
classification using windowed Fourier filters, R. Azencott and J. P. Wang
and L. Younes, Pattern Analysis and Machine Intelligence, IEEE
Transactions on 19 148--153 (1997)
This paper proposes to represent
texture using the energy (sum of squares) of Gabor
transforms over small windows. When textures are modeled as
stationary Gaussian random fields, these features can be
interpreted as non-parametric estimators of the spectral
density. This leads to the definition of a distance between
textures based on symmetrized Kullback-Leibler distances,
between stationary GRFs, which take a very simple form in
terms of spectral densities.
Synchronous Random Fields provide a representation for random fields over discrete grids that can be sampled from using massively parallel schemes that update all variables at the same time. The following papers introduce and study these models in the context of Image Processing and Neural Networks.
Boltzmann machines and curve identification tasks, R.
Azencott and A. Doutriaux and L. Younes, Network: Computation in
Neural Systems 4 461--480 (1993)
Synchronous random fields and image restoration, L Younes, Pattern Analysis and Machine Intelligence, IEEE Transactions on 20 (4), 380-390
Synchronous Boltzmann machines can be universal approximators, L. Younes, Applied Mathematics Letters 9 109--113 (1996)
Representation of Gibbs fields with Synchronous Random Fields., L. Younes, Markov Processes and Related Fields, vol. 2, 285–316. (1996)
Synchronous image restoration, L Younes, Computer Vision—ECCV'94, 213-217 (1994)
Learning algorithms for extended models of Boltzmann machines, L Younes, ICPR, 602-602, 1994
A three tiered approach for articulated object action modeling and recognition, Le Lu, Gregory D Hager, Laurent Younes, NIPS (2004)
invariant ATR, D. Bitouk and M. I.
Miller and L. Younes, Pattern Analysis and Machine
Intelligence, IEEE Transactions on 27
Asymptotic performance analysis for object recognition in clutter, D. Bitouk and M. I. Miller and L. Younes, Proceedings of SPIE 5094 101--108 (2003)
Empirically generated metric spaces for ATR in clutter, D. Bitouk and M. Miller and L. Younes, Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on 2 1407--1410 (2002)